Matthew Barsalou

Matthew Barsalou is an engineering quality expert in BorgWarner Turbo Systems Engineering GmbH’s global engineering excellence department. He is a Smarter Solutions certified Lean Six Sigma Master Black Belt, ASQ-certified Six Sigma Black Belt, quality engineer, and quality technician, and a TÜV-certified quality manager, quality management representative, and auditor. He has a bachelor of science in industrial sciences, a master of liberal studies with emphasis in international business, and has a master of science in business administration and engineering from the Wilhelm Büchner Hochschule in Darmstadt, Germany. He is author of the books Root Cause Analysis: A Step-By-Step Guide to Using the Right Tool at the Right Time, Statistics for Six Sigma Black Belts and The ASQ Pocket Guide to Statistics for Six Sigma Black Belts.

Abstract

This presentation covers empiricism in root cause analysis. Most professionals in quality will need to perform a root cause analysis, whether to optimize a process or identify the cause of a failure. Unfortunately, empiricism is an often neglected aspect. Both the theoretical and practical aspects of using the scientific method together with Box’s iterative inductive deductive process will be explained and an easy to use approach will be presented. This presentation will describe how to conduct an empirical root cause analysis. This includes using Tukey’s Exploratory Data Analysis to form tentative hypotheses that can be evaluated using more empirical approaches. The scientific method will also be covered as well as the steps necessary to conduct an empirical root cause analysis presented in the form of an easy to follow illustration. The final major concept will pertain to performing an empirical root case analysis when dealing with a customer claim for a quality failure.

A brief mention of reasons for performing a root cause analysis will be given followed by typical tools. A case will be made of using empirical root cause analysis. This includes the example of a consultant that beloved it was better to perform a Quality Function Deployment, before going into production to view failed components.

The correct application of the scientific method will be explained. This includes concepts such as what makes a good hypothesis as well as the need for a hypothesis to be refutable to be of any practical value. John Platt’s version of the scientific method will be explained using examples and these concepts will be combined into a Plan-Do-Check-Act process.

Exploratory Data Analysis can be used to evaluate the available data to form the first tentative hypothesis. This could mean reviewing customer complaints or looking at measurement data from a production process. These hypotheses should not be considered the definitive root cause of an issue, more robust forms of analysis should be performed. This approach provides a starting point for a failure analysis when little contrite information is available.

The concept of empirical root cause analysis will be depict in the form of a graphic which will show the various steps to combine Exploratory Data Analysis, the scientific method, Box’s iterative inductive deductive process into a Plan-Do-Check-Act like method.

This final section of the presentation will provide advice on how both the customer and supplier can better prepare for a complaint. The view of both customers and suppliers will be represented including information the customer must provide with a claim as well as how a supplier can better prepare for a customer issue.

Attendees will understand how to properly perform an empirical root cause analysis. This includes exploring data graphically, forming tentative hypothesis and evaluating the hypotheses empirically.